Collision avoidance may be important in many applications, such as Advanced driver-assistance systems (ADAS), industrial automation, and robotics. Conventional collision avoidance systems are commonly known to reduce the severity or occurrence of a collision or provide a forward collision warning.
In an industrial automation setting, certain areas are commonly off limits to a vehicle (e.g., automated vehicles or non-automated vehicles) for the protection of people and high value assets where damage is to be avoided. These areas may be quarantined by mapping (e.g., GPS coordinates, geofencing, and the like) or defined by markers that delineate a no-entry area. Collision avoidance systems may then be used to avoid the no-entry area or restricted space, which protects people and/or high value assets.
One common problem with conventional collision avoidance systems may come from detecting and reacting to false positives. For example, a collision avoidance system may suffer from false positives when detecting objects/markers and not delineating intended markers and non-intended reflective surfaces (such as a worker safety vest). The detection of false positives typically results in sub-optimal performance due to the response of the control system to all detections. Control response to false detections may result in unnecessary action which results in reduction of efficiency. The impact of false positive detection to an autonomous/semi-autonomous system is application specific. Tolerance of false positive detection may be integrated into the system design. The capability of a sensing platform to an application may be defined by false positive detection as well as missed true detection. Other common problems encountered with collision avoidance systems using some types of sensors may be the inability to handle varying degrees of illumination and the inability to differentiate colors.
To address one or more of these types of problems, there is a need for a technical solution that may be deployed to enhance ways to avoid collisions causing damage to logistics vehicles (such as cargo tractors and associated dollies) and doing so in an enhanced manner that improves system performance and helps reduce false positives. In particular, what is described are various exemplary types of delineation methods and systems where an industrial vehicle may use a light detection and ranging (LiDAR) sensor and multiple color cameras to detect beacons as types of markers, and deploy one or more model-predictive control systems to stop the vehicle from entering a restricted space as a way to avoid damage or contact with high value assets and provide an enhanced implementation of object detection and avoidance.